用核费雪判别分析构建树集成模型，提升分类准确率。
A Kernel Fisher Discriminant Analysis-Based Tree Ensemble Classifier: KFDA Forest
- 基于KFDA的树集成方法，通过核技巧处理非线性数据
- 在UCI和KEEL数据集上表现优于现有集成方法
- 适合需要高精度分类的非线性数据场景
通常，集成分类器比单个分类器更准确。本文提出一种名为核费雪判别分析森林（KFDA Forest）的集成分类器，是一种基于树的集成方法，结合了核费雪判别分析（KFDA）。为增强多样性，采用自助采样，并将变量集随机划分为K个子集。对每个子集执行KFDA，以提高分类精度。KFDA通过最大化类间距离、最小化类内距离来优化分类效果。借助核技巧，KFDA可将输入空间映射到核特征空间（即旋转空间），无需降维即可处理非线性数据结构。由于新特征轴与KFDA投影方向平行，因此选用决策树作为基分类器。为验证性能，将该方法应用于来自UCI和KEEL数据集的真实数据集，结果表明其优于现有集成方法。
原文摘要 · Abstract (English)
In general, an ensemble classifier is more accurate than a single classifier. In this study, we propose an ensemble classifier called the kernel Fisher discriminant analysis forest (KFDA Forest), which is a tree-based ensemble method that applies KFDA. To promote diversity, bootstrap is used, and variable sets are randomly divided into K subsets. KFDA is performed on each subset to increase classification accuracy. KFDA maximizes the distance between classes while minimizing the distance within classes. KFDA can also be applied to classification problems in a nonlinear data structure using the kernel trick because it can transform the input space into a kernel feature space, commonly named a rotation, rather than performing a dimensionality reduction. Because new feature axes and KFDA projections are parallel, decision trees are used as a base classifier. To compare the proposed method with existing ensemble methods, we apply these to real datasets from the UCI and KEEL repositories.
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